Forecasting system and method using change data based database storage for efficient asp and web application

ABSTRACT

In one embodiment, a system is provided. The system includes a first client. The system also includes an analysis server coupled to the first client. The system further includes a first customer database of information coupled to the analysis server. The first customer database is to embody forecast data and to receive essentially real-time updates to the forecast data. The first customer database supports an OLAP cube associated with the analysis server.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional application of and claims the benefitof priority to U.S. patent application Ser. No. 11/116,143 filed Apr.26, 2005 entitled REAL-TIME FORECASTING, now U.S. Pat. No. 7,447,718issued Nov. 4, 2008, which application claims priority to U.S.Provisional Patent Application Ser. No. 60/565,758, filed Apr. 26, 2004,each of which is hereby incorporated herein by reference.

BACKGROUND

Data is used by the investment and business communities to judge thevalue of investments, the business case for transactions, theperformance of managers, and trends in industries, among many otherthings. Data may be used in other areas and by other communities to makejudgments and decisions on a variety of matters. As such, data for acompany or organization is important in general, and up-to-date data(such as projected sales data for example) is potentially invaluable.However, forecast data in a company is typically stored in formats orsystems which are not amenable to updates on an asynchronous or randombasis.

For example, forecasting cycles are often based on monthly and quarterlyupdates to information. Government regulations often require reportingon no more than a quarterly basis (every three months). Activities inthe real world rarely occur on scheduled dates for updates, a customermay cancel or enhance an order at any time. Moreover, indirect actionswith direct effects on customers (such as competitor productannouncements or vendor supply changes for example) are also rarelycoordinated with a time which is convenient based on accountingschedules.

Thus, it may be advantageous to provide a system which allows forupdates on a random or asynchronous basis. Additionally, information inthe form of projections is often based on judgment. Thus, it may beadvantageous to provide a system in which changes may be made toinformation based on judgments made after input of such information.Moreover, some information (datapoints) may have particularsignificance. Thus, it may be advantageous to provide a system in whicha user may be notified of changes to particular datapoints.

SUMMARY

The present invention is described and illustrated in conjunction withsystems, apparatuses and methods of varying scope. In addition to theaspects of the present invention described in this summary, furtheraspects of the invention will become apparent by reference to thedrawings and by reading the detailed description that follows. A methodand apparatus for forecasting data with real-time updates is described.

In one embodiment, the invention is a system. The system includes afirst client. The system also includes an analysis server coupled to thefirst client. The system further includes a first customer database ofinformation coupled to the analysis server. The first customer databaseis to embody forecast data and to receive essentially real-time updatesto the forecast data. The first customer database may support an OLAPcube associated with the analysis server.

The analysis server may incorporate an OLAP cube therein, with the OLAPcube to analyze and update the information of the first customerdatabase. The first customer database may be dedicated to use by a firstset of selected users of the system. An identification server coupled tothe first client and coupled to the first customer database may also beincluded. The first client may be a smart client, and the first clientmay include its own OLAP cube. A second client may also be coupled tothe identification server and the analysis server. Moreover, a secondcustomer database of information may be coupled to the identificationserver and the analysis server, with the second customer databasededicated to use by a second set of users of the system. The informationmanipulated by the system may be financial information.

The first customer database may include watches of data. The firstcustomer database may also include sales forecast data. The firstcustomer database may further include sales forecast data with changesfrom non-sales personnel.

In another embodiment, the invention is also a system. The systemincludes an analysis server including an OLAP cube. The system alsoincludes an information database coupled to the analysis server tosupport the OLAP cube. The information database is to embody forecastdata and to receive essentially real-time updates to the forecast data.The system also may include an identification server coupled to theinformation database. The system may further include a client coupled tothe analysis server and to the identification server. Moreover, theinformation database may store financial information.

In yet another embodiment, the invention is a method of maintaininginformation. The method includes receiving a set of forecast data. Themethod also includes incorporating the forecast data into a database ofthe information through an OLAP cube. The method further includesextracting a baseline forecast from the database. Also, the methodincludes receiving updates to the database. Moreover, the methodincludes propagating updates almost immediately throughout theinformation through the OLAP cube.

The method may also include watching a set of watched data points of theinformation. The method may further include notifying a user of changesin the set of watched data points responsive to the propagating andreceiving updates. Similarly, the method may also include providinginformation to a user. The method may further include receiving expectedchanges of the information from the user. The method may also includepropagating the expected changes as updates almost immediatelythroughout the information through the OLAP cube.

Additionally, the method may include reviewing updates received andpropagated through the information. The method may include providinguser-readable updates of information responsive to the reviewing. Theforecast data may be financial information. Also, the method may beexecuted by a processor in response to instructions, with theinstructions embodied in a machine-readable medium. Moreover, the methodmay include receiving actual data corresponding to the information. Themethod may further include comparing the actual data to the informationand providing user-readable comparisons of information and the actualdata.

In one embodiment, the invention is a method. The method includesreceiving forecast information from sales people in a computer. Themethod further includes receiving comments on specific entries of theforecast information from non-sales people in the computer. The methodalso includes receiving changes of the forecast information from thenon-sales people in the computer. The method additionally includesproviding a display of the comments and the changes to the sales peoplein an interface to the computer.

The comments may be received from marketing and executive personnel.Forecast information and associated comments and changes may be storedin a database and OLAP cube associated with the computer. The computermay actually be a network of computers hosting the database and OLAPcube. The method may be executed by a processor in response toinstructions, with the instructions embodied in a machine-readablemedium.

The method may further include receiving requests to watch specific dataof the forecast information. Moreover, the method may include providingupdates when specific data changes responsive to the requests to watch.Similarly, the method may include receiving judgments from the non-salespeople related to a set of data of the forecast information and changingthe set of data of the forecast responsive to the judgments. The methodmay also include analyzing the changes for largest magnitude changes.The method may further include providing a set of impacts to the salespeople, the impacts showing the changes based on a rank ordering ofmagnitude of the changes.

Moreover, the comments and changes may be received through devices otherthan the computer that are coupled to the computer for communication.Similarly, the forecast data may be received through devices other thanthe computer that are coupled to the computer for communication.Additionally, the interface to the computer may be through devices otherthan the computer that are coupled to the computer for communication.

In yet another embodiment, the invention is a system. The systemincludes a user interface to receive forecast data. The system alsoincludes an analysis server including an OLAP cube. The system furtherincludes an information database coupled to the analysis server tosupport the OLAP cube. The information database is to embody theforecast data and to receive essentially real-time updates to theforecast data through the user interface. The user interface is todisplay the forecast data through the user interface with feedback asthe data changes over time.

In still another embodiment, the invention is a method of providingfeedback from members of an organization on forecasts to sales peopleentering data for the forecasts. The method includes receiving the datafor the forecasts from the sales people in a computer. The method alsoincludes storing the data for the forecasts within a database inconjunction with an OLAP cube as forecast information. The methodfurther includes receiving comments on specific entries of the forecastinformation in the OLAP cube from the members of the organization in thecomputer. The method also includes receiving changes of the forecastinformation from the members of the organization in the computer.Moreover, the method includes integrating the comments and the changesinto the OLAP cube and the database. Also, the method includes providinga display of the comments and the changes to the sales people in aninterface to the computer.

In still another embodiment, the invention is a method. The methodincludes receiving forecast data in a database with an associated windowvalue. The method also includes accessing data based on associatedwindow values. The method further includes comparing the data accessedbased on associated window values to other data.

In another embodiment, the invention is a method of maintaininginformation. The method includes receiving a set of forecast data. Themethod also includes incorporating the forecast data into a database ofthe information through an OLAP cube. The method further includesextracting a baseline forecast from the database. The method alsoincludes receiving updates to the database. The method further includespropagating updates almost immediately throughout the informationthrough the OLAP cube. The method also includes extracting an updatedbaseline forecast from the database, the updated baseline forecastderived from the baseline forecast.

In another embodiment, the invention is a method. The method includesreceiving forecast information in a computer from a first group ofusers. The method also includes integrating the forecast informationinto a database and corresponding OLAP cube accessible by the computer.The method further includes receiving comments on specific entries ofthe forecast information in the computer from a second group of users.The method also includes receiving changes of the forecast informationin the computer from a second group of users. The method furtherincludes integrating comments and changes into the database and OLAPcube. Moreover, the method includes providing a display of the commentsand the changes to the first group of users in an interface to thecomputer.

The present invention is exemplified in the various embodimentsdescribed, and is limited in spirit and scope only by the appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated in various exemplary embodimentsand is limited in spirit and scope only by the appended claims.

FIG. 1 illustrates information flowing through an organization.

FIG. 2 illustrates information for a sales person in one embodiment.

FIG. 3 illustrates information for a customer in one embodiment.

FIG. 4 illustrates information for a region in one embodiment.

FIG. 5 illustrates information for a larger region in one embodiment.

FIG. 6 illustrates information for worldwide operations of a company inone embodiment.

FIG. 7 illustrates an embodiment of a network which may be used inconjunction with forecasting data with real-time updates.

FIG. 8 illustrates an embodiment of a machine which may be used inconjunction with forecasting data with real-time updates.

FIG. 9 illustrates an embodiment of a system for use in forecasting datawith real-time updates.

FIG. 10 illustrates an alternate embodiment of a system for use inforecasting data with real-time updates.

FIG. 11 illustrates an embodiment of a method for use in forecastingdata with real-time updates.

FIG. 12 illustrates an alternate embodiment of a method for use inforecasting data with real-time updates.

FIG. 13 illustrates display by product of information in an embodiment.

FIG. 14 illustrates display by customer of information in an embodiment.

FIG. 15 illustrates display of a specific impact in an embodiment.

FIG. 16 illustrates an operations report for information in anembodiment.

FIG. 17 illustrates an inventory report for information in anembodiment.

FIG. 18 illustrates a gap report for information in an embodiment.

FIG. 19 illustrates a drilled down display of information for a salesperson in one embodiment.

FIG. 20 illustrates an updated forecast in an embodiment.

FIG. 21 illustrates a drilled down display of a region in an embodiment.

FIG. 22 illustrates application of judgment in an embodiment.

FIG. 23 further illustrates application of judgment in an embodiment.

FIG. 24 illustrates a forecast after application of judgment in anembodiment.

FIG. 25 illustrates change history for information in an embodiment.

FIG. 26 illustrates addition of a watch in an embodiment.

FIG. 27 illustrates an embodiment of a process of providing feedback ina real-time forecasting system.

FIG. 28 illustrates relationships between various groups and dataassociated with those groups in an embodiment.

FIG. 29 illustrates another embodiment of a process of providingfeedback in a real-time forecasting system.

FIG. 30 illustrates a real-time forecasting system and its relationshipwith various groups in an embodiment.

FIG. 31 illustrates an embodiment of a process of providing salesfeedback in a real-time forecasting system.

FIG. 32 illustrates changes to an atomic data point in one embodiment.

FIG. 33 illustrates an embodiment of a process of generating a baseline.

FIG. 34 illustrates an embodiment of a process of operating a real-timeforecasting system in conjunction with a baseline.

FIG. 35 illustrates relationships between baselines and potentialoperations in an embodiment.

FIG. 36 illustrates an embodiment of a process of gathering data in areal-time forecasting system.

FIG. 37 illustrates an embodiment of a process of annotating data in areal-time forecasting system.

FIG. 38 illustrates an embodiment of a process of displaying annotateddata in a real-time forecasting system.

FIG. 39 illustrates an embodiment of a process of revising annotateddata in a real-time forecasting system.

FIG. 40 illustrates an embodiment of a process of operating a real-timeforecasting system.

FIG. 41 illustrates a timeline of operations in conjunction with areal-time forecasting system in one embodiment.

FIG. 42 illustrates an embodiment of a data structure.

FIG. 43 illustrates an embodiment of an OLAP structure.

FIG. 44 illustrates an embodiment of a process of making changes in anOLAP structure.

FIG. 45 illustrates an embodiment of a process of responding to a queryin an OLAP structure.

FIG. 46 illustrates an embodiment of a data structure in conjunctionwith a timeline.

FIG. 47 illustrates an embodiment of a process of responding to arequest for data.

FIG. 48 illustrates an embodiment of a process of storing a change indata.

FIG. 49 illustrates an embodiment of a set of baselines and trackeddata.

FIG. 50 illustrates an embodiment of a medium and associated devices.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The present invention is described and illustrated in conjunction withsystems, apparatuses and methods of varying scope. In addition to theaspects of the present invention described in this summary, furtheraspects of the invention will become apparent by reference to thedrawings and by reading the detailed description that follows. A methodand apparatus for forecasting data with real-time updates is described.In general, the method and apparatus relate to gathering forecast datafrom a variety of sources, developing a baseline forecast from thegathered data, and updating the baseline forecast based on essentiallyreal-time changes in data as gathered from the variety of sources andother data sources. Moreover, the method and apparatus allow for viewingof forecast data with updates and may allow for simulation or alterationof the data.

In one embodiment, the invention is a system. The system includes afirst client. The system also includes an analysis server coupled to thefirst client. The system further includes a first customer database ofinformation coupled to the analysis server. The first customer databaseis to embody forecast data and to receive essentially real-time updatesto the forecast data. The first customer database may support an OLAPcube associated with the analysis server.

The analysis server may incorporate an OLAP cube therein, with the OLAPcube to analyze and update the information of the first customerdatabase. The first customer database may be dedicated to use by a firstset of selected users of the system. An identification server coupled tothe first client and coupled to the first customer database may also beincluded. The first client may be a smart client, and the first clientmay include its own OLAP cube. A second client may also be coupled tothe identification server and the analysis server. Moreover, a secondcustomer database of information may be coupled to the identificationserver and the analysis server, with the second customer databasededicated to use by a second set of users of the system. The informationmanipulated by the system may be financial information.

The first customer database may include watches of data. The firstcustomer database may also include sales forecast data. The firstcustomer database may further include sales forecast data with changesfrom non-sales personnel.

In another embodiment, the invention is also a system. The systemincludes an analysis server including an OLAP cube. The system alsoincludes an information database coupled to the analysis server tosupport the OLAP cube. The information database is to embody forecastdata and to receive essentially real-time updates to the forecast data.The system also may include an identification server coupled to theinformation database. The system may further include a client coupled tothe analysis server and to the identification server. Moreover, theinformation database may store financial information.

In yet another embodiment, the invention is a method of maintaininginformation. The method includes receiving a set of forecast data. Themethod also includes incorporating the forecast data into a database ofthe information through an OLAP cube. The method further includesextracting a baseline forecast from the database. Also, the methodincludes receiving updates to the database. Moreover, the methodincludes propagating updates almost immediately throughout theinformation through the OLAP cube.

The method may also include watching a set of watched data points of theinformation. The method may further include notifying a user of changesin the set of watched data points responsive to the propagating andreceiving updates. Similarly, the method may also include providinginformation to a user. The method may further include receiving expectedchanges of the information from the user. The method may also includepropagating the expected changes as updates almost immediatelythroughout the information through the OLAP cube.

Additionally, the method may include reviewing updates received andpropagated through the information. The method may include providinguser-readable updates of information responsive to the reviewing. Theforecast data may be financial information. Also, the method may beexecuted by a processor in response to instructions, with theinstructions embodied in a machine-readable medium. Moreover, the methodmay include receiving actual data corresponding to the information. Themethod may further include comparing the actual data to the informationand providing user-readable comparisons of information and the actualdata.

In one embodiment, the invention is a method. The method includesreceiving forecast information from sales people in a computer. Themethod further includes receiving comments on specific entries of theforecast information from non-sales people in the computer. The methodalso includes receiving changes of the forecast information from thenon-sales people in the computer. The method additionally includesproviding a display of the comments and the changes to the sales peoplein an interface to the computer.

The comments may be received from marketing and executive personnel.Forecast information and associated comments and changes may be storedin a database and OLAP cube associated with the computer. The computermay actually be a network of computers hosting the database and OLAPcube. The method may be executed by a processor in response toinstructions, with the instructions embodied in a machine-readablemedium.

The method may further include receiving requests to watch specific dataof the forecast information. Moreover, the method may include providingupdates when specific data changes responsive to the requests to watch.Similarly, the method may include receiving judgments from the non-salespeople related to a set of data of the forecast information and changingthe set of data of the forecast responsive to the judgments. The methodmay also include analyzing the changes for largest magnitude changes.The method may further include providing a set of impacts to the salespeople, the impacts showing the changes based on a rank ordering ofmagnitude of the changes.

Moreover, the comments and changes may be received through devices otherthan the computer that are coupled to the computer for communication.Similarly, the forecast data may be received through devices other thanthe computer that are coupled to the computer for communication.Additionally, the interface to the computer may be through devices otherthan the computer that are coupled to the computer for communication.

In yet another embodiment, the invention is a system. The systemincludes a user interface to receive forecast data. The system alsoincludes an analysis server including an OLAP cube. The system furtherincludes an information database coupled to the analysis server tosupport the OLAP cube. The information database is to embody theforecast data and to receive essentially real-time updates to theforecast data through the user interface. The user interface is todisplay the forecast data through the user interface with feedback asthe data changes over time.

In still another embodiment, the invention is a method of providingfeedback from members of an organization on forecasts to sales peopleentering data for the forecasts. The method includes receiving the datafor the forecasts from the sales people in a computer. The method alsoincludes storing the data for the forecasts within a database inconjunction with an OLAP cube as forecast information. The methodfurther includes receiving comments on specific entries of the forecastinformation in the OLAP cube from the members of the organization in thecomputer. The method also includes receiving changes of the forecastinformation from the members of the organization in the computer.Moreover, the method includes integrating the comments and the changesinto the OLAP cube and the database. Also, the method includes providinga display of the comments and the changes to the sales people in aninterface to the computer.

In still another embodiment, the invention is a method. The methodincludes receiving forecast data in a database with an associated windowvalue. The method also includes accessing data based on associatedwindow values. The method further includes comparing the data accessedbased on associated window values to other data.

In another embodiment, the invention is a method of maintaininginformation. The method includes receiving a set of forecast data. Themethod also includes incorporating the forecast data into a database ofthe information through an OLAP cube. The method further includesextracting a baseline forecast from the database. The method alsoincludes receiving updates to the database. The method further includespropagating updates almost immediately throughout the informationthrough the OLAP cube. The method also includes extracting an updatedbaseline forecast from the database, the updated baseline forecastderived from the baseline forecast.

In another embodiment, the invention is a method. The method includesreceiving forecast information in a computer from a first group ofusers. The method also includes integrating the forecast informationinto a database and corresponding OLAP cube accessible by the computer.The method further includes receiving comments on specific entries ofthe forecast information in the computer from a second group of users.The method also includes receiving changes of the forecast informationin the computer from a second group of users. The method furtherincludes integrating comments and changes into the database and OLAPcube. Moreover, the method includes providing a display of the commentsand the changes to the first group of users in an interface to thecomputer.

FIG. 1 illustrates information flowing through an organization.Organization 100 may be typical of companies or other organizationsconcerned with finances. CEO 110 needs financial information as a basicpart of the job. Sales organization 150 is thus asked to prepare aforecast of sales. This forecast may span months or years for example.Examples illustrated are for 6 months, but other time periods may fitcircumstances in various situations. Other forecasts may be requestedwithin an organization within the spirit and scope of the presentinvention. For example, forecasts of inventory or expenses may beprepared and tracked in real-time.

Within sales organization 150, sales representatives 175 provideforecasts of their upcoming sales. Similarly, independent representative180 and distributor 190 provide forecasts of upcoming sales. Areamanager 170 receives these forecasts, and passes them up to vicepresident (VP) of sales 160. VP 160 then passes the forecasts tomarketing department 140. At this point, and at previous links, feedbackor analysis of the financial data (forecasts) may occur, such as throughchanges to estimates, requests for information about or verification ofdata, or other forms of feedback or analysis.

Marketing 140 then sends current data to production 130 (engineering andmanufacturing for example). Production 130 may comment and providechanges based on manufacturing considerations (such as delays orstockpiles for example) and then pass the information to finance 120.Finance department 120 may comment and provide changes based onfinancial considerations, such as availability of capital or status ofaccounts (such as past due accounts for example). Finance department 120then passes the updated forecast data to CEO 110 as a baseline forecast.CEO 110 may use this baseline for managerial analysis and for referencewhen speaking to non-members of the organization, such as news mediaoutlets, customers, vendors and regulators for example. With a staticforecast, the data may be stale by the time CEO 110 sees it. Withreal-time updates, CEO 110 may rely more effectively on available datato analyze and comment on the organization's financial situation.

To illustrate in further detail the forecasting and update process,reference may be made to how data is provided initially. Again, theprocess is presented in terms of sales data, but data of various typesmay be forecasted and tracked in real-time. FIG. 2 illustratesinformation for a sales person in one embodiment. The information isentered and/or displayed through user interface 200. Field or frame 260is a display of information for the sales person 220. As illustrated,this is a display in currency (such as dollars for example) for asalesperson 220 including a company 230, a part 240 for that company,details 245 for the part 240, and another part 250 for which details arenot presently selected. Field or frame 260 is a user interface forproducts sold by salesperson 220 which allows for entry of forecast datarelated to specific products for the salesperson 220.

In one embodiment, a sales representative or similar individual (user)enters information into each cell in frame 260, and is required to“touch” each cell (enter or confirm data in the cell) to attempt toverify that no data is inadvertently left out or entered incorrectly.Moreover, the user may be required to touch each cell of the summarydata of part 270. Additionally, status information related to what isbeing entered is displayed as status 205, and submit 210 and exit 215buttons are provided for submission of entered data and exit of thesoftware respectively. Once data has been entered, a similar userinterface may be used to display the data. If changes are made to thedata, those changes may also be displayed as described below.

As illustrated, a similar user interface 300 may be used for display ofinformation once it is entered. FIG. 3 illustrates information for acustomer in one embodiment. Interface 300 provides a forecast overview,impact messages, top 10 customers, and navigation tools. Forecastdisplay 310 provides information about a particular sales representative(for all sales people) in one embodiment. The information is displayedin a cell format, with sales people separated by row and columns devotedto time periods. Display 360 indicates what is being displayed, in thiscase a representative sales inc. in the NE USA part of the east USAregion. Moreover, selector 365 determines the format of the display 310,in this case a summary, time selector 370 determines the displayed timeperiod, and type selector 375 determines the type of display, such asdollars or units. Additionally, display 310 provides for user selectionof a method of viewing (by months as illustrated) and a type of view(table as illustrated).

Another part of the display is the impacts message display 315, whichprovides messages about impacts to a projection based on changes. Stillanother part of the display is top 10 customers display 305, which maybe used to provide forecasts on the top 10 customers in real time,regardless of what else is displayed. Additionally, status andnavigation tools are provided. Forecast button 335 leads to thedisplayed forecast data. Drill down button 330 allows a user to delveinto details of an entry of a subset of displayed data. Settings button325 allows the user to change settings of the display. Help button 320allows the user to access online help and potentially to access helpover a network for example. Identity 345 displays an identity of thecurrent user, and projection status 340 displays the status of theprojection (such as whether it needs to be approved or it is active andwill provide updates). Moreover, logout button 355 and home button 350allow for exiting the system or navigating to a predetermined home partof the system respectively.

With information from users related to various customers and areas, anoverview of a broader area may be provided. FIG. 4 illustratesinformation for a region in one embodiment. By navigating to a differentregion, or a region encompassing the previously displayed data forexample, information for a region may be displayed. In this example, thedisplay of frame 310 now provides data for sales inc., as the data forthe NE USA region. Display 360 indicates what region is displayed. Notethat the data displayed for sales inc. is an aggregation of the datadisplayed in FIG. 3, as this is essentially displaying data at a higherlevel of abstraction or a different level of organization from the dataof FIG. 3.

Aggregation of data for larger regions may similarly proceed. FIG. 5illustrates information for a larger region in one embodiment. Display310 now provides data for the NE USA and SE USA regions, with the NE USAregion aggregating the sales inc. data of FIG. 4. Similarly, the SE USAregion aggregates appropriate data. As may be expected, display 360indicates what is being displayed, in this case the East USA area.

As one may expect, aggregation may ultimately go to a worldwide level.FIG. 6 illustrates information for worldwide operations of a company inone embodiment. User interface 300 now provides data on a worldwidebasis, with an indication of what area is displayed in display 360. Inparticular, display 310 provides data for east USA, west USA, and AsiaPacific regions. The east USA data is an aggregation of the data of FIG.5. Moreover, as illustrated, no changes have been made to theinformation. Additionally, the display 310 allows for display by area orentity (tab 380), by customer (tab 385), and by product (tab 390).However, having gathered the information and displayed it, it may beuseful to manipulate the information, such as by various users over anetwork using individual clients or workstations for example.

The following description of FIGS. 7-8 is intended to provide anoverview of computer hardware and other operating components suitablefor performing the methods of the invention described above andhereafter, but is not intended to limit the applicable environments.Similarly, the computer hardware and other operating components may besuitable as part of the apparatuses of the invention described above.The invention can be practiced with other computer systemconfigurations, including hand-held devices, multiprocessor systems,microprocessor-based or programmable consumer electronics, network pcs,minicomputers, mainframe computers, and the like. The invention can alsobe practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network.

FIG. 7 shows several computer systems that are coupled together througha network 705, such as the internet. The term “internet” as used hereinrefers to a network of networks which uses certain protocols, such asthe TCP/IP protocol, and possibly other protocols such as the hypertexttransfer protocol (http) for hypertext markup language (html) documentsthat make up the world wide web (web). The physical connections of theinternet and the protocols and communication procedures of the internetare well known to those of skill in the art.

Access to the internet 705 is typically provided by internet serviceproviders (ISPs), such as the ISPs 710 and 715. Users on client systems,such as client computer systems 730, 740, 750, and 760 obtain access tothe internet through the internet service providers, such as ISPs 710and 715. Access to the internet allows users of the client computersystems to exchange information, receive and send e-mails, and viewdocuments, such as documents which have been prepared in the htmlformat. These documents are often provided by web servers, such as webserver 720 which is considered to be “on” the internet. Often these webservers are provided by the ISPs, such as ISP 710, although a computersystem can be set up and connected to the internet without that systemalso being an ISP.

The web server 720 is typically at least one computer system whichoperates as a server computer system and is configured to operate withthe protocols of the world wide web and is coupled to the internet.Optionally, the web server 720 can be part of an ISP which providesaccess to the internet for client systems. The web server 720 is showncoupled to the server computer system 725 which itself is coupled to webcontent 795, which can be considered a form of a media database. Whiletwo computer systems 720 and 725 are shown in FIG. 7, the web serversystem 720 and the server computer system 725 can be one computer systemhaving different software components providing the web serverfunctionality and the server functionality provided by the servercomputer system 725 which will be described further below.

Client computer systems 730, 740, 750, and 760 can each, with theappropriate web browsing software, view html pages provided by the webserver 720. The ISP 710 provides internet connectivity to the clientcomputer system 730 through the modem interface 735 which can beconsidered part of the client computer system 730. The client computersystem can be a personal computer system, a network computer, a web TVsystem, or other such computer system.

Similarly, the ISP 715 provides internet connectivity for client systems740, 750, and 760, although as shown in FIG. 7, the connections are notthe same for these three computer systems. Client computer system 740 iscoupled through a modem interface 745 while client computer systems 750and 760 are part of a LAN. While FIG. 7 shows the interfaces 735 and 745as generically as a “modem,” each of these interfaces can be an analogmodem, ISDN modem, cable modem, satellite transmission interface (e.g.“direct pc”), or other interfaces for coupling a computer system toother computer systems.

Client computer systems 750 and 760 are coupled to a LAN 770 throughnetwork interfaces 755 and 765, which can be ethernet network or othernetwork interfaces. The LAN 770 is also coupled to a gateway computersystem 775 which can provide firewall and other internet relatedservices for the local area network. This gateway computer system 775 iscoupled to the ISP 715 to provide internet connectivity to the clientcomputer systems 750 and 760. The gateway computer system 775 can be aconventional server computer system. Also, the web server system 720 canbe a conventional server computer system.

Alternatively, a server computer system 780 can be directly coupled tothe LAN 770 through a network interface 785 to provide files 790 andother services to the clients 750, 760, without the need to connect tothe internet through the gateway system 775.

FIG. 8 shows one example of a conventional computer system that can beused as a client computer system or a server computer system or as a webserver system. Such a computer system can be used to perform many of thefunctions of an internet service provider, such as ISP 710. The computersystem 800 interfaces to external systems through the modem or networkinterface 820. It will be appreciated that the modem or networkinterface 820 can be considered to be part of the computer system 800.This interface 820 can be an analog modem, isdn modem, cable modem,token ring interface, satellite transmission interface (e.g. “directpc”), or other interfaces for coupling a computer system to othercomputer systems.

The computer system 800 includes a processor 810, which can be aconventional microprocessor such as an Intel Pentium microprocessor orMotorola Power PC microprocessor. Memory 840 is coupled to the processor810 by a bus 870. Memory 840 can be dynamic random access memory (dram)and can also include static ram (SRAM). The bus 870 couples theprocessor 810 to the memory 840, also to non-volatile storage 850, todisplay controller 830, and to the input/output (I/O) controller 860.

The display controller 830 controls in the conventional manner a displayon a display device 835 which can be a cathode ray tube (CRT) or liquidcrystal display (LCD). The input/output devices 855 can include akeyboard, disk drives, printers, a scanner, and other input and outputdevices, including a mouse or other pointing device. The displaycontroller 830 and the I/O controller 860 can be implemented withconventional well known technology. A digital image input device 865 canbe a digital camera which is coupled to an I/O controller 860 in orderto allow images from the digital camera to be input into the computersystem 800.

The non-volatile storage 850 is often a magnetic hard disk, an opticaldisk, or another form of storage for large amounts of data. Some of thisdata is often written, by a direct memory access process, into memory840 during execution of software in the computer system 800. One ofskill in the art will immediately recognize that the terms“machine-readable medium” or “computer-readable medium” includes anytype of storage device that is accessible by the processor 810 and alsoencompasses a carrier wave that encodes a data signal.

The computer system 800 is one example of many possible computer systemswhich have different architectures. For example, personal computersbased on an Intel microprocessor often have multiple buses, one of whichcan be an input/output (I/O) bus for the peripherals and one thatdirectly connects the processor 810 and the memory 840 (often referredto as a memory bus). The buses are connected together through bridgecomponents that perform any necessary translation due to differing busprotocols.

Network computers are another type of computer system that can be usedwith the present invention. Network computers do not usually include ahard disk or other mass storage, and the executable programs are loadedfrom a network connection into the memory 840 for execution by theprocessor 810. A web TV system, which is known in the art, is alsoconsidered to be a computer system according to the present invention,but it may lack some of the features shown in FIG. 8, such as certaininput or output devices. A typical computer system will usually includeat least a processor, memory, and a bus coupling the memory to theprocessor.

In addition, the computer system 800 is controlled by operating systemsoftware which includes a file management system, such as a diskoperating system, which is part of the operating system software. Oneexample of an operating system software with its associated filemanagement system software is the family of operating systems known asWindows® from Microsoft Corporation of Redmond, Wash., and theirassociated file management systems. Another example of an operatingsystem software with its associated file management system software isthe Linux operating system and its associated file management system.The file management system is typically stored in the non-volatilestorage 850 and causes the processor 810 to execute the various actsrequired by the operating system to input and output data and to storedata in memory, including storing files on the non-volatile storage 850.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present invention, in some embodiments, also relates to apparatusfor performing the operations herein. This apparatus may be speciallyconstructed for the required purposes, or it may comprise a generalpurpose computer selectively activated or reconfigured by a computerprogram stored in the computer. Such a computer program may be stored ina computer readable storage medium, such as, but is not limited to, anytype of disk including floppy disks, optical disks, CD-ROMS, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), eproms, eeproms, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description below.In addition, the present invention is not described with reference toany particular programming language, and various embodiments may thus beimplemented using a variety of programming languages.

Various networks and machines such as those illustrated in FIGS. 7 and 8may be utilized. FIG. 9 illustrates an embodiment of a system for use inforecasting data with real-time updates. System 900 includes clients,servers, and supporting databases. Various embodiments of systems may beused, with different configurations as needed due to circumstancessurrounding an implementation or installation of such a system. Thesystem 900 may be used with various types of data which is suitable forboth forecasting and for essentially real-time updates. Essentiallyreal-time updates refer to updates provided to the system as soon aspracticable—this may be immediate, or it may occur as soon as a personwith the appropriate information is in a position to relay thatinformation to the system 900.

System 900 includes a client portion, with clients 910 and 970, a serverportion with identification server 920 and analysis server 940, and adatabase portion with databases 930, 950 and 960. In one embodiment,databases 930, 950 and 960 are each dedicated to specific customers(such as a first customer, second customer and third customer).Identification or authentication server 920 received access requestsfrom various clients such as clients 910 and 970. Server 920 thenauthenticates or identifies the client(s) and current users to determinewhich database (if any) should be accessible. Analysis server 940 thenstarts receiving requests from the clients, allowing for analysis ofdata in the selected database.

Authentication and access may be handled in various ways. For example,once a client (client 910 for example) is authenticated, it may beredirected to the analysis server 940 with something such as a tokenwhich encodes an address for server 940 and information about whichdatabase (such as 930) to use. Alternatively, client 910 may receive akey (such as a portion of a public key-private key pair for example),which may be used to access a previously known address for server 940(the key may enable a response for example). The key may also berequired to be transmitted from server 940 to database 930 to accessdata, for example.

Analysis server 940 may be implemented in part using an OLAP cube. OLAPcubes are available from various commercial entities, such as MicrosoftCorporation of Redmond, Wash., for example. An OLAP cube may performautomated analysis of data when supported by a database such as database930 for example, allowing for fast throughput of data and fastpropagation of changes. In one embodiment, all analysis occurs at server940, as client 910 is a local client used only to submit information andqueries and to view information in the user interface. In anotherembodiment, client 970 is a smart client which incorporates someanalysis capabilities (such as through a local OLAP cube and repositoryfor example). Client 970, as a smart client, can then be operated inisolation from the network and server 940, while still allowing foranalysis and display of data actually stored or replicated at client970.

As illustrated, FIG. 9 reflects a system which may be distributedgeographically and organizationally, thus allowing for revenuegeneration based on access to and maintenance of the system for example.FIG. 10 illustrates an alternate embodiment of a system for use inforecasting data with real-time updates. Site 1000 includes a client1010 (potentially many clients), a server 1020 and a database 1030. Asthe system is self-contained, authentication may or may not be needed(site 1000 may be restricted to authorized users for example). Client1010 may be implemented as a local client without analyticalcapabilities, or as a smart client, and server 1020 may be implementedfor analysis with an OLAP cube for example.

How the systems and other embodiments operate may vary. FIG. 11illustrates an embodiment of a method for use in forecasting data withreal-time updates. The method (1100) and other methods of this documentare illustrated as a set of process modules which may be rearranged andmay be performed or operated in a parallel or series manner, forexample. At module 1110, data is received, such as financial data for aninitial forecast. At module 1120, the data is propagated in the system(such as in an OLAP cube or supporting database). At module 1130, aforecast baseline is formed, such as due to arriving at a deadline ordue to a determination that enough data has been collected for example.

With the baseline available, some information about the forecast maychange, and other information may remain static. At module 1140, changesare received. At module 1150, the received changes are propagated orprocessed, with updates propagating through the system, alerts ornotifications being sent, and any recorded adjustments being applied asappropriate. Modules 1140 and 1150 may be executed multiple times in aniterative fashion as changes are received, thus allowing for essentiallycontinuous and almost real-time updates. Moreover, as time comes for thenext forecast (the next quarter for example), the process may return tomodule 1110.

Specific processes may be utilized in some embodiments. FIG. 12illustrates an alternate embodiment of a method for use in forecastingdata with real-time updates. At module 1210, initial data of a forecastis received, such as raw data from sales representatives for example. Atmodule 1220, the data received is propagated through the system. Atmodule 1230, revisions to data, such as judgments applied to raw dataleading up to a forecast are received. Such judgments may come fromsupervisors of sales representatives, marketing department personnel,manufacturing or engineering personnel, financial personnel, and eventhe CEO or other high-level personnel. Modules 1210, 1220 and 1230 maybe repeated due to data arriving at different times or other variationsin input cycles for example. At module 1240, the baseline is formed,with forecast data from modules 1210, 1220 and 1230.

At module 1250, changes are received, such as updates to previouslyforecast data. While some changes may be confirmations of forecasts(change from expectation to actual), many changes may be actual changesas orders come in at different prices, quantities and the like fromforecasted data. At module 1260, a determination is made as to whetherthe actual changed data was watched. If so, at module 1265, notificationis sent based on the watch request, with a level of detail appropriateto the request for the watch and system capabilities. At module 1270,the changes are propagated through the system, such as through an OLAPcube and/or database. At module 1275, the various datapoints that areaffected are checked to determine if any of them are watched. If so, atmodule 1280, notification is provided based on the watch request. Atmodule 1290, impacts to the forecast prior to receipt of the changeddata are shown, preferably in a manner allowing for easy userinterpretation of the data. At module 1295, judgment or adjustments arereceived from users responsive to the changed information, resulting inthe receipt of further changes at module 1250 and so forth.

Further aspects and features of an embodiment may be understood withreference to a user interface for the embodiment and a description ofhow and why the interface changes. FIG. 13 illustrates display byproduct of information in an embodiment. As mentioned, information maybe displayed by region, for example. As illustrated, display 310provides information by product, with similar or the same information.The display 360 indicates that this is a display by product, for allfamilies in the illustrated example. Just as the impacts may be providedin impact display 315 for regions, they may also be provided forproducts, such that impacts may be displayed based on the view of thedata provided in display 310. Alternatively, a user may specify thatimpacts are provided at a global level, or otherpredetermined/preselected level for that user for example.

FIG. 14 illustrates display by customer of information in an embodiment.Again, the option of displaying information in a different way isillustrated. Rather then display by either part or region, display bycustomer allows for a determination of which customers are increasingorders, decreasing orders, or experiencing short-term changes forexample. As the data is preferably stored with associations tocustomers, parts, regions, sales persons, and other relevantinformation, displaying the data in various different ways becomespossible. Moreover, as the data populates an OLAP cube, shifting betweendisplays involves simply shifting from displaying one aspect of theinformation as it is maintained in the OLAP cube to displaying adifferent aspect of the information as it is already maintained in theOLAP cube. Note that the data may be displayed responsive to selectingthe tab 385 (marked customers) and that display 360 indicates how thedata is viewed.

As illustrated above, the data in FIG. 13 and FIG. 14 is displayedbefore a forecast is compiled into a baseline. However, changes in datamay occur at any time, particularly after the baseline is formed. FIG.15 illustrates display of a specific impact in an embodiment. Impactsare translations into human understandable language (such as English forexample) of changes in data as propagated through the system. In theinstance illustrated, impacts display 315 is expanded to allow forcloser examination of the impacts reported. Moreover, one may drill downinto the impacts, to determine how the impacts came about or what thesource of the impact is. Additionally, impacts may occur before, duringor after formation of a baseline forecast. Thus, status indicator 340indicates these impacts are being viewed after a forecast has beenaccepted (or after the user no longer has options for input), andfurther indicates that continuous updates will be received.

Other presentations of the data are also available. FIG. 16 illustratesan operations report for information in an embodiment. Operationsdisplay 405 provides a view of the data based on an operational ormanufacturing viewpoint, with information about changes in demand orabsolute demand quantities illustrated and a summary of changes as well.Again, drilling down may also be an option, such that demand for a partmay be broken down into when the demand will occur or why the changesare occurring.

FIG. 17 illustrates a forecasted change to inventory report forinformation in an embodiment. Unlike the view in operations display 405of FIG. 16, the inventory report illustrated in operations display 405focuses more on monetary value of the change impact of forecast updates,with both a summary and detailed information. Such a report may also beprovided for services based on capacity to fulfill demand and actualdemand.

Likewise, a gap report may also be provided in operations display 405.FIG. 18 illustrates a gap report for information in an embodiment. Thegap report may provide a view of variances between a projection andcurrent actual numbers, and thus indicate what orders need to happen toensure that the company delivers on its expected forecast.

Drilling down is typically available, unless a user is restricted fromsuch an action. FIG. 19 illustrates a drilled down display ofinformation for a sales person in one embodiment. Based on an earlierprojection, data for a single sales person may be displayed by drillingdown on a display of data for multiple sales people (such as was foundin FIG. 3 for example). Drill-down display 410 provides the details ofthe data making up the entry for one sales person. Moreover, if the userhas authority to do so, apply judgment button 415 allows for entry ofjudgment about whether the forecast is accurate, or how it needs tochange. This will be described further below.

Updates may occur while someone is viewing data such as drilled-downdata, or when a user is offline. FIG. 20 illustrates an updated forecastin an embodiment. Note that changes may have occurred in the forecastfor various reasons. The display 310 provides summary data, with some ofthe data flagged by balls indicating changes in the forecast. Suchchanges may be the result of applications of judgment (post-forecast) orof actual changes to forecasted data based on real-time updates. Impactsdisplay 315 may provide further information in some instances, such aswhen an actual change provides an impact.

In pursuing information about updates, one may view drilled-down dataand corresponding changes or information. FIG. 21 illustrates a drilleddown display of a region in an embodiment. Drill-down display 425presents data specific to the region selected in this illustration, suchas sub-region-specific, part-specific and customer-specific information.Thus, drill-down display 425 may present a different view from thatprovided in display 310 for example. Moreover, add watch button 420allows a user to watch a given data point for changes, over a certainthreshold, within a certain time, or otherwise, for example.

Both judgments and watches may be understood with reference to furtherillustrations. FIG. 22 illustrates application of judgment in anembodiment. Judgment interface 430 includes value change option 440 andpercentage change option 460. Value change option 440 includes type 445(units or revenue for example) and value 450 (amount of change forexample). Percentage change option 460 includes direction 465(increase/decrease), type 470 (revenue or units for example) and amount475 (the amount of change for example). As illustrated, a change byvalue is executed, with a set amount provided—the end result of thechange is specified. With a change by percentage, the amount of change(delta) is specified. Additionally, because a change judgment) is beingentered, status 340 indicates that an approval must be provided for thechange to take effect.

FIG. 23 further illustrates application of judgment in an embodiment. Inthis instance, since a judgment has already been applied, judgmenthistory 480 is populated with an entry which is displayed. This mayallow a user to avoid a redundant judgment entry, or may provide contextfor future applications of judgment. Again, a judgment is being enteredin value change option 440, thus further changing the data in question.

FIG. 24 illustrates a forecast after application of judgment in anembodiment. With the change entered, a view of the original dataprovides an indication that judgment has been applied. In particular,judgment indicator balls 510 are now present, indicating the presence ofjudgment history associated with the displayed data or with dataaggregated into the displayed data. As one may expect, drilling down tothe changed data will allow one to see a judgment history such ashistory 480 of FIG. 23. FIG. 25 illustrates change history forinformation in an embodiment. By drilling down, or by selecting thejudgment indicator ball 510, the history 520 is displayed, includinginformation about who entered the judgment, type of change, who itaffected, and the amount of the change. In the illustrated example, thesales person responded to the judgment after the forecast was made byproviding a further correction.

While judgments change the forecast, watches indicate changes, eitheractual or forecast. FIG. 26 illustrates addition of a watch in anembodiment. Watch window 530 pops up when application of a watch isselected, and includes change type 535 in which the amount of a changeto be watched for is entered. Thus, the user can control whether a verysmall change, a catastrophic change, or some intermediate changetriggers the watch. Typically, a watch sends an email or similar messageto the user setting the watch once the predetermined threshold isreached or exceeded (missed or undershot).

When data is changed from an original value to a new value, such as inresponse to a request to apply judgment, this may have a number ofeffects. This may be a change in percentage or absolute terms forexample, and may result from specific expectations or information, orfrom general expectations (hunch, intuition, etc.) For example. Thechange to the data results in a propagated change to other data.Alternatively, a change could be made to other data with changesback-propagated to some or all of the data contributing to the changeddata. Moreover, if a piece of data is set for watching, withnotification to the user should the data change, and a change propagatesthrough, this may trigger the watch alert. Watching may be set for anychange, changes above a specified threshold, or changes before a certaindate for example. Also, watches may not be required to detect changesthat would be visible, such that if the display generally is inbillions, a change in the thousands may be sufficient to trigger anotification.

While illustrations of changes have been provided on a high-level basis(the whole world for example), changes of unit quantities may alsooccur. Information for a single product may be displayed over ageographical area, rather than for a geographical area for all productsfor example. Information may also be displayed for a set of customersfor a single product or product line, for example, or on other bases.Moreover, the information may be displayed in units of product ratherthan currency. Preferably, information on both a currency number and aunits (quantity) number is stored. Thus, display of information in theuser interface may be shifted between the two types of displays. Theunits may be a physical quantity (number of parts or devices forexample), an estimated physical quantity (number of meals served forexample), or some form of service metric (number of hours billed forexample). Moreover, the various datapoints may be absolute numbers orscaled (such as quantity in thousands for example). Also, watches andjudgments may be applied on a quantity basis rather than a currencybasis.

Because specific products or services may be tracked, updates may bebased on changes in a single order or a long-term relationship forexample. A customer may decide to exit a business or discontinue aproduct, thus ending a need for a purchased component for example.Similarly, a vendor may decide to discontinue a product, thus requiringa customer to ramp up purchase to ensure an adequate stockpile after thecomponent is discontinued. Such resulting updates may be a confirmationor cancellation or other change to a forecasted order, for example.Moreover, such updates may propagate further up. Additionally, anyjudgments may be dynamic (reduce a number by 10% always for example), ormay be conditional (reduce a forecast number to x until it decreases tox). Thus, propagation may stop prior to reaching every related datapoint.

Further Considerations for Financial Embodiments

The following description of an exemplary system related specifically tofinancial data provides details of an embodiment, along withimplementation details which may be incorporated in various embodiments.The features and details may be used in part in other embodiments withinthe spirit and scope of the present invention, and may be combined withother features and details described previously. In particular, most ofthe details provided are appropriate for many types of data, and are notrestricted to financial data.

The system, in one embodiment, lets companies streamline the process ofcreating a bottom-up forecast of sales and financial data. In oneembodiment, this includes collecting the data from those on the frontlines. This may include receiving data from sales representatives,distributors, representative firms, customers, retailers, or othersources of forecast data. Following this, the data may be aggregatedwithin the system, and the system may then allow sales and marketingmanagement to apply judgment. The hierarchical judgment applications maybe tracked, such as by maintaining data about judgments applied tospecific data and to corresponding changes to other data. Moreover,prior to or after applying judgment, the consequences of the judgmentapplied may be understood, as the changes flow through the data (asimplemented by the OLAP cube), and are displayed. Moreover, analyticaltools may also be employed to understand the data. Examples of suchtools include regression analysis, statistical analysis, data mining,and correlation analysis among other tools.

After an initial review of the data provided, a sales vice president orsimilar person in authority may create an official forecast baseline,preferably after the person has understood, judged and approved the datathat has been rolled up to him. At this point, the person may releasethe data for others in the company to consume; and define the baselineagainst which updates will be tracked. The baseline may be yearly,quarterly, monthly, bi-weekly, weekly, even daily if desired, and may beimplemented on some other time-frame. Moreover, multiple users orpersons in authority may play a role in building the forecast ormodifying the forecast, such as by allowing for marketing input forexample. Thus, some portions of the forecast may come from marketing;marketing may apply judgment at some point in the process; or marketingmay provide longer-term forecasting (in contrast to shorter-term salesforecasting) for example.

The released forecast then provides departments within the company, suchas production, engineering and operations, insight into what will needto be built, both when and where. Moreover, this allow for vetting (andthus feedback) from production, and may allow for prediction of trendsfor parts or supplies for example. Similarly, this allows financedepartments to analyze and predict financial data such as a grossmargin, either on a line-by-line level or at an enterprise level, forexample. This then allows for planning of capital needs and forsimulation or ‘what if’ type of scenarios, both within the system or ina separate financial system. Additionally, finance departments canprovide feedback to the baseline as well, such as by indicating whichaccounts are doubtful and should be discouraged until payment is morereasonable, or by indicating what expected financial trends may do tovarious industries.

From this, the CEO may then see all perspectives of the forecast, alongwith the broad overview of the forecast. This allows the CEO to obtain‘one number’ for the entire company—allowing for intelligent discussionswith media and outside interests when the CEO interacts with the public.Alternatively, the CEO can track a set of numbers and associatedrelationships, allowing the CEO to also understand the perspectiveswithin the company which relate to these numbers. Moreover, thisprovides a clear and detailed view of expected developments of thecompany. As the CEO may also simulate changes, apply judgments or watchnumbers (along with other departments and people), the CEO and staff maythen analyze potential changes. As such, this facilitates key decisionsa CEO may need to make. Such decisions, whether made by the CEO or someother member of the company, may include determining what parts toretire and when; how much of internal resources to allocate; where toinvest based on what appears to be driving the business; how tostreamline internal operations; and how to maximize capital efficiencyfor example.

With the baseline in place, not only analysis and feedback, but alsoreal-time updates are available. The system may show the impact ofchanges if they pass a certain threshold, and show the impacts bydepartment, group or otherwise. This then allows the company to react toexternal (market for example) forces, allows all groups to consider andagree on options to handle changes, and allows for a group or consensusdecision on whether or not to choose specific options.

In one embodiment, the process may be described as follows:

Data collection is automated, to the extent possible. This may includesending out automated reminders to those generating the data (orinputting the data they observe for example). The users or forecasters(who may be sales people with many other demands on their time) enterdata using a simple user interface which is robust enough to trap orcatch common errors such as entering too many or too few zeros,unintentionally large or small changes, or incomplete data or omissionsfor example. Moreover, the user may be provided data to help create theright forecast. For example, a backlog may be used as a starting point,or the last forecast may be used as such a starting point. Additionally,feedback may be provided on forecast accuracy, such as by attempting tocurb over-optimistic forecasts or sandbagging.

With data entered, or with a deadline approaching or past, notificationsmay be sent to those who are tasked with reviewing and judging the data.Notices may relate to delinquent forecasts, forecasts in and ready forapproval, or problems with forecast creation for example. The systemprovides tools to view the data, view aggregated data (which may beviewed or drilled down to various levels), apply judgment (globally orlocally for example), send forecasts back forrework/correction/reconsideration for example, and approve the forecast(and/or send to the next hierarchical level).

Preferably, the interface is simple, with controls that change globalperspective (such as switching to a graphic or tabular view forexample). This may further include providing comparisons to various timeperiods (such as a previous forecast, successive quarters, year on yearcomparisons, and current information versus baseline comparisons forexample). Similarly, comparisons to actual sales or actual data may beprovided.

Moreover, data may be shown in various forms, or with various aspects ofthe overall collection of data displayed, and data may be drilled downfrom higher to lower levels of data aggregation, ultimately to atomicdata levels. Thus, data may be provided as revenue, units, gross margincurrency, gross margin percentage, simple margin currency, simple marginpercentage, average selling price, or in any other format eithercollected or derivable from collected data. Similarly, data may besliced by various means, such as by region, customer, part (or service),or by some custom aggregation of atomic data or previously aggregateddata for example. A custom aggregation may allow for display of data byprogram, segment or other varying groupings of data for example.

Moreover, data may be displayed based on various breakdowns. Forexample, users can drill into any value at any point by clicking and maythen see what makes it up. Thus, users may break down data on parts,customers, or regions for example. Similarly, users can then click onany sub-entity (such as a division of a customer or a part of a regionfor example) to get further breakdowns.

In some embodiments, baseline creation and continuous updates formseparate tasks and may have separate interfaces. Thus, a differentbusiness process may be used for each. Creating a baseline is oftenoriented towards a bottom-up commit—sales people providing commitmentsto the company for example. Creating updates is often more of a changenotification prompting or requiring decisions and/or action—the biggestcustomer starts canceling orders and high-level executives need to actright away.

With the data stored in the system, this enables continuous updates—achange may be propagated essentially immediately. The system mayimmediately notify all groups to a change in terms that make the impact(of the change) clear. These groups may include sales, marketing,production, engineering, operations, finance, and executive groups forexample. Moreover, the updates may be provided using a simple client orinterface, and may be tied to production to ensure that sales peoplewill get goods for their customers in the right quantities at the righttimes.

While continuous and essentially real-time updates provide a fairlyaccurate picture as changes occur, judgment may be used to predictchanges, and judgment histories may be employed to determine whenpredicted changes occur, or whether predictions are already integratedinto a forecast. Judgments may be applied hierarchically to forecastdata. Thus, a judgment may be applied to a high-level number, and thenchanges may be cascaded or propagated to lower levels, allowing users tosee the effects of the judgment.

Tracking and inspecting the history of judgments applied (by attachingjudgments and judgment history to data for example) allows users todetermine whether judgments should be reversed for example. Moreover,variances of actual results from judgments may be examined and analyzed.Additionally, as judgment may be applied prior to or after a baseline isformed, differences of pre-baseline versus post baseline judgment may betracked.

While judgments allow for predictions, watches allow for action. Everyuser has the opportunity to design custom notifications around anythingthey can see from their vantage point in the system. Thus, the user maychoose to watch a data point for a particular customer, part, service,time period, or other data point, as long as it is visible. The user mayset a custom threshold for notification, and a users may create and savecollections of watches with corresponding messages that can be turnedinto reports for example.

Further analysis and related judgments and watches may be applied tocustom aggregations of data. Preferably, the system lets customersaggregate data in any way they choose. For example, part data may beaggregated based on programs. Customer data may be aggregated based onmarket segments, region data may be aggregated based on climates,selling entity data (salespeople, representative, etc.) may beaggregated based on performance. Each of these aggregations areexemplary, and illustrative of the options for aggregation provided inpart due to use of the OLAP cube. These aggregations may be employed fora variety of purposes, including simple reporting, analysis, or exportto other systems for example.

Impacts of changes are often an important goal of analysis, watches,judgments, aggregations or any other exercises carried out on financialdata. The system automatically determines what the biggest impacts areand displays them by absolute rank and relative changepercentage—basically by tracking changes as they occur and maintaining aset of lists of such information. This allows users to drill down intosources of the biggest impacts. Moreover, impacts are provided accordingto whatever the drill down perspective is, (such as through contextsensitive impacts) in some embodiments.

Multiple Organization Considerations

The system described above can be used profitably by a singleorganization. However, having multiple organizations using the samesystem requires that data be segregated to satisfy confidentialityconcerns, for example. As indicated previously, data repositories may beseparate, either logically or physically (or both). Moreover, processingfacilities may also be separated, logically and/or physically. Thus, afirst user may use a first client to access a system, and thereby accessa first data repository using an OLAP cube. A second user may use asecond client to access the system. In so doing, the second user mayaccess a second data repository, and would preferably use a differentOLAP cube or at least a different instantiation of an OLAP cube orcubes.

Thus, each user, or at least each organization, has access to OLAP cubeswhich in turn work only with data from a dedicated repository. Moreover,each dedicated repository may have associated with it customizations forthe organization and/or user in question. Thus, an instance of an OLAPcube may effectively be customized for the user or organization whenaccess to the dedicated repository is granted.

By providing dedicated instances of OLAP cubes and dedicatedrepositories, a flexible structure which may be hosted across multipleservers is formed. This supports providing a web-hosted application,such as through use of ASP technology. Multiple users or organizationsmay be supported through dedicated repositories, dedicated instances ofOLAP cubes, and shared supporting software and physical resources.Similarly, this architecture does not tie down the location of physicalresources, allowing for either distributed resources (such asgeographically separated servers and networks for example) orconcentrated resources (such as server farms for example).Considerations such as geographic diversity/redundancy or ease ofmaintenance may come into play because the technology allows for suchflexibility.

Further Examples of Various Embodiments

Specific details of various embodiments may be understood with referenceto the following description and accompanying figures. The variousembodiments may share features with other embodiments, such that anembodiment may incorporate features from several different embodimentsillustrated herein, even though those features are not specificallyillustrated together in a single embodiment. Similarly, variousembodiments need not include all of the features or aspects illustratedin an embodiment, provided the embodiment still meets the claims.

Feedback

One example of a feature present in some embodiments is a feedbackcapability. FIG. 27 illustrates an embodiment of a process of providingfeedback in a real-time forecasting system. Process 2700 relates toproviding such feedback, and includes collecting data (receiving data),receiving feedback on that data, providing the altered data or data withfeedback to the original user providing the original data, and receivingand checking for approval further changes to the data after feedback isreceived.

Process 2700 begins at module 2710 with receipt of data, such asforecast data entered by a field operative for an enterprise. At module2720, the entered data is integrated or submitted into a database, andmay also be populated into an OLAP cube, for example. The data is thenprovided for review at module 2730, presumably with data collected fromother field operatives. Feedback information is provided for the data atmodule 2740, including changes to data and other forms of feedback. Thisfeedback may take on various forms, and may be entered by various peopleor entities. At module 2745, the feedback data is integrated or enteredinto the corresponding database, and may also be populated in an OLAPcube, for example. Modules 2730, 2740 and 2745 may form a subprocesswhich is repeated for various people, organizations or groups, forexample.

At module 2750, a request is received for data in its current state froma field operative. Thus, the field operative is seeking data afterfeedback information has been provided. The altered data is provided inresponse to the request at module 2760, such as through a databaserequest for information related to a given user, for example. Thus, theuser may view information or data which is visible based on anassociated userid (e.g. Data the user has privileges for or isauthorized for) or may view data based on what information the useroriginally entered, for example. At module 2770, the user may provideobjections to changes to data, such as contesting changes to some or allof the data, or contesting the reasoning behind such changes ifavailable. These objections are received by the system, and may besubmitted for review by a predetermined user at module 2780—therebyallowing for intelligent adoption or rejection of objections or changes.At module 2790, the data in question is finalized, signaling that thedata will not change further, at least during the present dataentry/modification cycle.

In some embodiments, real-time forecasting may be used in commercialenterprises to forecast sales, production, and related revenues, forexample. FIG. 28 illustrates relationships between various groups anddata associated with those groups in an embodiment. System 2800represents the internal system for forecasting of a commercialenterprise, with relationships between various departments illustratedin an exemplary manner.

Sales group 2810 may present an initial sales forecast 2815,representing an expectation of what sales will be for an upcoming periodof time, such as an upcoming month. This sales forecast 2815 may providea baseline for comparison against actual performance and againstforecasts from other parts of the organization. Based on sales forecast2815, marketing group 2820 may then provide a marketing forecast 2825,providing input on how marketing thinks actual results will differ fromthe sales forecast 2815, and potentially providing reasoning fordifferences as well. This input may be provided on a macro or microlevel, either as a broad adjustment to an entire category (geographicalregion, customer, industry, product, for example) or to specificforecasts from particular people, for example. Thus, forecast 2825 mayform a marketing baseline in some instances.

Building on forecast 2825, finance group 2830 may then provide forecast2835, which may be expected to reflect finance group 2830 judgment aboutforecast 2825. The judgment reflected in forecast 2835 may reflectexpectations about cash flow, customer payments, inventory, or otherareas of concern tracked by finance group 2830. Similarly, operationsgroup 2840 may provide forecast 2845 based on forecast 2835 andexpectations about capacity or other operational factors. Finally,executive group 2850 may use forecast 2845 as a baseline to generateforecast 2855, after applying expectations of executive group 2850 tothe data of forecast 2845. Note that forecast 2855 may be dependent oncomponents of various forecasts or baselines, such that the financebaseline 2835 may be deemed more reliable, or the marketing baseline2825 may be viewed as trustworthy by executive group 2850. Similarly,this strategy may be used for parts of forecasts, thus allowing abaseline 2845 to be adopted for some areas, but allowing other areas ofan ultimate forecast to start from one of baselines 2815, 2825 or 2835,for example.

Preparation of baselines and achievement of related objectives involvesforecasting expectations, providing feedback on those forecasts, andreviewing objections to feedback. FIG. 29 illustrates another embodimentof a process of providing feedback in a real-time forecasting system.Process 2900 includes gathering data, presenting the data, gatheringfeedback, presenting the feedback, gathering objections to the feedback,and reviewing objections.

Process 2900 begins with gathering of forecast data at module 2910.Forecast data is then presented to reviewers at module 2920. Feedback(changes, commentary, for example) on the forecast data is gathered fromreviewers at module 2930. The feedback is then presented to providers ofthe forecast data at module 2940. Objections to the feedback are thengathered at module 2950, and those objections are reviewed asappropriate at module 2960.

Where data comes from (and where it goes) in a forecasting system mayillustrate how feedback is provided. FIG. 30 illustrates a real-timeforecasting system and its relationship with various groups in anembodiment. System 3000 includes a forecasting system, and interfaces(interaction) with sales, marketing, finance, operations and executivegroups. The information flowing back and forth between the forecastingsystem and the various groups provides for forecasting, feedback andchanges to forecasts, and training on future forecasts.

Forecast system 3060 includes a database 3070 which embodies forecastdata and an OLAP engine 3080 which interacts with database 3070 toprovide access to data in particularly flexible ways. Sales group 3010provides forecast data to system 3060 and receives back forecastfeedback information, such as indications of whether forecast data seemsunreasonable, has been changed due to market conditions, or has beenchanged due to prior forecast performance, for example. Sales group 3010may also provide commentary or objections to feedback, either due toorganizational commitments or due to differing judgments, for example.

Marketing group 3030 similarly reviews sales forecasts and providesoverall market data, either through specific data input or throughchanges and comments on sales forecast data. Finance group 3040receives/reviews an overall forecast, and provides information onfinancial constraints, such as through separate data inputs or throughsuggested changes to forecast data, for example. Similarly, operationsgroup 3050 reviews the current forecast data and provides data onproduction constraints, such as by adjusting forecast data for example.

All of this data interaction provides a forecast which executive group3020 may review and alter. Alteration may occur through application ofexecutive judgment to the forecast, based on knowledge of marketconditions, separate conversations with customers, or other sources ofknowledge. Such alteration may include commentary along with changes,and the act of alteration may be recorded to allow a user to understandhow changes occurred. Thus, sales group 3010 may provide forecast salesdata, marketing group 3030, operations group 3050 and finance group 3040may comment on and change the data, and executive group 3020 may alsoreview, alter and comment on the forecast data, to arrive at an overallforecast. Moreover, various groups may then review the overall forecastto determine how individual forecasts were changed or commented on byother groups, allowing for improved future forecasting and forunderstanding of expected changes in forecasts.

A specific process for sales forecasting and feedback may furtherillustrate the forecasting system. FIG. 31 illustrates an embodiment ofa process of providing sales feedback in a real-time forecasting system.Process 3100 includes receiving sales data, storing the data, presentingdata for review, receiving and integrating feedback, receiving a requestfrom a salesperson, presenting altered data, receiving objections andupdates, presenting objections and updates for approval, receiving suchapproval (or denial) and integrating data into the database.

Sales data in the form of forecasts for upcoming time periods isprovided at module 3110. This data is stored in a database (as new dataor changes) at module 3115. The data is then presented to otherstakeholders for review at module 3120. Such other stakeholders mayinclude marketing, production/operations, finance and executive groups,for example. Feedback on the forecast data is provided at module 3130.This feedback, in the form of changes to data, comments on data, andsimilar changes, is integrated into the database at module 3135. Adetermination is then made at module 3140 as to whether all feedback hasbeen received. This may relate to receiving feedback from alldepartments, or may relate to time for feedback ending, for example. Iffeedback is still to be received, the process returns to module 3120.

If all feedback is received, the process then moves to providingfeedback to users. A request from a salesperson for feedback is receivedat module 3150. This request may be an affirmative request from thesalesperson, or may simply be an indication that the salesperson haslogged into a system at a time when feedback is automatically provided.Altered data or data with commentary is presented at module 3160, suchas by allowing a salesperson to see what changes were made or see whatimpacts changes had. If the salesperson has objections to changes orupdates to data, such objections and updates are received at module3170. As these objections and updates (changes) are provided after theforecasting period, these changes may be reviewed at module 3180 priorto integration into the forecast. Those changes that are allowed areapproved at module 3190, and other changes are denied. The approvedchanges are then integrated into the database at module 3195.

Understanding potential changes and related feedback of an atomic datapoint may be useful. FIG. 32 illustrates changes to an atomic data pointin one embodiment. Chronolgy 3200 illustrates changes to such a datapoint. Data at point 3210 includes a forecast of 400 units for xyzcompany in the northeast. Point 3220 indicates feedback of a softermarket (less demand as seen by a marketing department for example)indicates only 350 units will be ordered. Point 3230 has no changes andno opinion expressed (such as a finance department seeing no need forchange).

Point 3240 illustrates a further downgrade in an order (or units to fillthe order) due to a production constraint (as may come from an operationor production group for example). Point 3250 includes an upgrade in theforecast, back to 350 units, indicating capacity should be found (suchas when an executive decision about satisfying a customer occurs forexample). Point 3260 illustrates feedback which may be seen by the salesperson entering the data, showing the various changes and relatedcommentary.

Note that each of the points in the chronology may also support abaseline, and some or all data points may be used as an objective to beachieved. The baseline may be generated due to timing of data entry(meeting a deadline) or due to a desire to capture a state of the system(taking a snapshot for later reference). Similarly, an objective may berelated to an organizational interest in meeting commitments, forexample.

Baselines

Generation of a baseline from which objectives may be measured can occurin a variety of ways. FIG. 33 illustrates an embodiment of a process ofgenerating a baseline. Process 3300 includes receiving data, integratingthe data into a database, generating a snapshot, and classifying thesnapshot as a specific baseline.

Process 3300 includes receiving data at module 3310. The data receivedmay be original data, or a change to existing data. If the data isoriginal, it will be included in the database at module 3320 as a newdata entry. If the data is a change, it will be included in the databasealong with context information about what the change is. Moreover, thedata may be a simple numerical value or change, or it may be a commentor annotation on an existing value, for example. All data receivedincludes a timestamp within the database, indicating when it wasentered. Receiving and integrating data continues until a cutoff pointis reached.

A determination of a cutoff is made at module 3330, and may occur in avariety of ways. Some cutoffs may be automatic, such as generation of abaseline on a preset day of the current week or month. Other cutoffs maybe manual, such as a request for a baseline from a user or department.When the cutoff occurs, a snapshot is generated at module 3340.

The snapshot may be implemented in a variety of ways, too. For example,a snapshot may be a recording of the time at which the baseline wasrequested, or a preset time for a baseline previously requested. Thissnapshot time marks which changes should be included to calculate abaseline, and which changes should be excluded as they occurred afterthe time of the baseline. Alternatively, a snapshot of the state of thesystem may be recorded, much like a backup of data in a static fashion.Such a backup may be resource and time-intensive to complete, but it mayalso facilitate calculation of differences between values in thebaseline or values of the baseline and other values later.

The snapshot is classified as a baseline at module 3350. This may simplyinclude assigning an identifier to the value of the snapshot (or thedata stored as the snapshot). Additionally, it may include designationof permissions related to users authorized to view a baseline. Forexample, a baseline may be a private baseline, created for personal useof a user or restricted use of a group. Alternatively, a baseline may berestricted to those having certain general access rights, although suchaccess rights may change over time, either in an evolutionary mannerwith an organization or in a cyclical manner, for example. Additionally,the baseline may be restricted in what it includes, such as byrestricting it based on various categories within the data such ascustomer, data source (marketing and operations but not sales, forexample), or by restricting it based on how far back data may beretrieved to provide the baseline.

Baselines may be used in a variety of ways. FIG. 34 illustrates anembodiment of a process of operating a real-time forecasting system inconjunction with a baseline. Process 3400 includes receiving andintegrating data, comparing data to established baselines, checking forcutoff points for generation of baselines, and generation of baselines.

Data is received at module 3410, and is then integrated into a databaseat module 3420. This integration may also include integration into anOLAP server and data structure, as will be described in greater detailbelow. If a comparison is desired (requested by a user) then adetermination is made to perform a comparison at module 3430. Such ascomparison occurs at module 3440, using a baseline previouslyestablished as one of baselines 3450. Thus, a user may determine howmuch a current forecast as represented by data in the database hasvaried from a baseline forecast established at a previous time.Alternatively, the user may determine how much two different baselinesdiverged, allowing for post-mortem analysis of changes in data made byvarious users, and for analysis of differences between forecasts andresults.

At module 3460, a determination is made as to whether a cutoff point hasbeen reached. If not, the process returns to receiving data at module3410. If so, such as due to a predetermined cutoff or a request for abaseline, a snapshot is generated at module 3470. This snapshot may begenerated as it would be for module 3340 of FIG. 33. The information ofthe baseline is then classified as a baseline at module 3480 and addedto baselines 3450 for future use in comparisons. Note that suchclassification may be similar to that of module 3350 of FIG. 33.

Also, note that baselines may, and typically will, be derived fromearlier baselines. Typically, baselines will derive in a sequentialfashion, one from the previous one. However, private baselines may bederived in some instances, and such baselines may be derived from aselected subset of past data as incorporated in various baselines. Thewindows discussed later may be used for this purpose, with only some ofthe earlier windows specified for a particular private window orbaseline.

Baselines may provide a variety of opportunities for analysis. FIG. 35illustrates relationships between baselines and potential operations inan embodiment. Data collection 3500 includes multiple baselines andillustrates results of analysis that may be achieved. Baseline 3510 isan original baseline in a forecast, such as a baseline established aftersales data is initially received. Revised baseline 3520 is a laterrevised baseline, which may include input from a variety of sources, andpotentially has different data from that in baseline 3510. Actualresults 3530 are the actual results of operations corresponding to theforecasts of baselines 3510 and 3520.

Comparison of this data results in various analytical results. Variance3540 represents differences between two sets of data, and illustrateswhere the forecast did not meet reality. Accuracy 3550 represents howfar away a forecast was from reality or a later forecast—accuracy 3550and variance 3540 may represent the same data, and either present thedata differently, or present different aspects of the data, such aspercentage versus absolute magnitude, for example.

Distortions 3560 represents identified factors distorting the forecast,such as market changes, customer defections, unpredictable events, andother issues which may account for differences between a prediction anda result. Tendency 3570 represents identified trends in predictions, asmay be represented by tracking forecasts over time or tracking resultsof forecasts by users over time. Tendency 3570 may includeidentification of forecasters who are unpredictable (unreliablyinaccurate), regularly optimistic, regularly pessimistic, or generallyaccurate, for example. Similarly, tendency 3570 may illustratecorrelations between different forecasts, or between forecasts andresults, for example. Correction 3580 may include an identification offactors which may be used to correct future forecasts. This may includeidentifying users who should be trained to forecast better. It may alsoinclude identifying offsets or multipliers which may be used to correctforecasts of those who are reliably incorrect, for example.

Annotations

While baselines allow for much comparison of results, annotation ofresults reinforces or potentially corrects predictions during thepredictive process. Moreover, annotation of data may raise issues(either actual or anticipated) at an early stage to allow for discussionand resolution of such issues. Annotations may be gathered after initialdata is gathered, to indicate whether the initial data looks incorrectto users of the system, and allow for more meaningful interactionbetween users that simple changes to numbers allows.

Thus, the data and annotation gathering process may include gatheringdata, annotating data, displaying annotated data, and revising annotateddata. FIG. 36 illustrates an embodiment of a process of gathering datain a real-time forecasting system. Process 3600 includes receivingforecast data, integrating data into the database, determining datagathering should stop, and halting gathering of data.

Forecast data is received at module 3610. This may include initialforecast data from various users such as field operators or salespeople, and may relate to what is expected in various timeframes.Typically, the forecast data is a personal forecast which includesbusiness information which is expected to be useful to the organization.The forecast data is integrated into a database 3650 at module 3620.This integration may be entry of initial data with associatedparameters, or may be entry of a change to a prior forecast in the caseof longer term forecasts. At some point, a cutoff point may be reached,such as an end of a forecasting period. This is determined at module3630, and when the cutoff is reached, the data entry process halts atmodule 3640, such as through locking of database 3650 to further changesor changes from designated users. Note that database 3650 may include aclassical database, and an associated OLAP structure as well.

With initial forecast data entered, the forecast is then reviewed byother users. FIG. 37 illustrates an embodiment of a process ofannotating data in a real-time forecasting system. Process 3700 includespresenting data to users, receiving adjustments and comments,integrating adjustments and comments into the data base, and completingannotation entry.

At module 3710, data from database 3650 is presented to a user or users.This typically occurs on a wide scale, allowing various users to reviewand change or comment on data. Adjustments to data are received atmodule 3720, and comments are received at module 3730. Note that suchadjustments (actual changes) and comments (notes about the data) may bereceived essentially simultaneously, and for a variety of different datapoints. At module 3740, the adjustments and comments are integrated intodatabase 3650. If the annotation process is complete, this is determinedat module 3750. If it is not, then the process returns to module 3710for presentation of data, whereas if the process is complete, module3760 halts entry of annotations.

In some embodiments, the initial forecast data is compiled from fieldoperatives such as sales people. Annotations then come from marketinggroups, production/operations groups, finance groups and executivegroups. Thus, the process of modules 3710 through 3750 may be repeatedseveral times for different groups or users, even for the same piece ofatomic data. Also, later users may be able to see some or allannotations from earlier users, allowing for further commentary oravoiding duplicative (and overly cumulative) changes.

After entry of annotations, the data may be reviewed by users whooriginally entered it, along with the annotations. FIG. 38 illustratesan embodiment of a process of displaying annotated data in a real-timeforecasting system. Process 3800 includes receiving a request forannotated data, retrieving the requested data, and presenting the datato the requestor.

At module 3810, a request for annotated data is received from a user.For example, a sales person may wish to see what marketing, productionand finance did with previous projections and whether desiredcommitments will be met. Alternatively, a sales person may want to seewhat feedback is available about the overall market or the overallcustomer relationship. At module 3820, requested data is retrieved fromdatabase 3650. Note that requested data is likely to be data requestedby the user through a user attempt to access the data, rather than aspecific request from the user. At module 3830, the requested data, nowretrieved from database 3650 along with associated annotations, isprovided to the user, such as through a graphical user interface. Notethat if the user has limited rights to access the data, some annotationsmay not be available to the user in question.

The nature of human interaction is that disagreements arise. FIG. 39illustrates an embodiment of a process of revising annotated data in areal-time forecasting system. A sales person may disagree with changesto forecasts, either because specific information contradicts a moregeneral change or because the sales person simply believes the data tobe inaccurate. Both situations may be addressed.

Process 3900 includes receiving comments from data reviewed, integratingcomments into the database, and finalizing comments in the database. Asales person or similar forecaster may provide comments about datapresented with annotations at module 3910. Such comments may contestannotations and changes, request further changes, or indicate changesare agreeable, for example. At module 3920, these comments areintegrated into the database 3650. This integration process may simplybe recording the comments, or may involve an effort to alert asupervisory user to the comments. At module 3930, comments in thedatabase are finalized. This may include review by a supervisory user,review by those affected by comments, or some other form of check on thereason for changes.

Note that annotations may involve more active functions than simplyproviding an opinion or change. For example, in some embodiments,annotations not only raise an issue through a comment, but also providean indication of a lack of confidence in part or all of a data pointwithout specifically changing that data point. For example, if a salesperson indicates 500 parts will be ordered by a customer six monthslater, that can be annotated to indicate it is contingent on thecustomer designing in the specified part in a new product.

Thus, the annotation may not only state the concern, but include a tagindicating how much the number should be reduced to handle thecontingency. If the part would be ordered in a quantity of 300 unitswithout the design-in, the annotation may indicate 200 units arecontingent on a design-in decision. If the decision is slated for amonth after the annotation is made, the annotation may be implemented tonotify the user making the annotation, and the user whose forecast wasannotated, to check on the decision. Such notifications may be emailmessages sent at specified or predetermined times, and may also show upalong with impacts in a dashboard display, for example.

The overall process of data collection, annotation, and furtherrevision, may be even more involved in some embodiments. FIG. 40illustrates an embodiment of a process of operating a real-timeforecasting system. Process 4000 illustrates such collection of data,collection of changes and annotations, collection of objections, and useof the data in both forecasting and review of execution. The actualimplementation of this process and similar processes may vary in termsof order of operations, use of all modules, or addition of some modules,for example.

A sales forecast is received from a user or set of users at module 4010.This sales forecast may be initial data related to expected sales, forexample. At module 4015, the sales forecast is revised. This may includerevisions by those originally entering the data, or revisions by othersin the sales group, such as superiors. At module 4020, a baseline isgenerated using the sales forecasts from the sales group/department.

At module 4025, marketing group updates to the forecast are received.These may be annotations, either comments or changes, entered by variousmarketing personnel. Such changes may relate to market conditions,overall relationships with customers, and later arriving data, forexample. At module 4030, a finance group update is similarly received.The finance group may provide information related to financingconstraints or financial market conditions which impact sales forecasts.In a similar manner, an operations or productions set of inputs isreceived at module 4035. This may include production constraints,inventory considerations, other supply chain issues, and allocations ofproduction capacity, for example. All of these inputs may then be usedto generate a new baseline at module 4040. The inputs of modules 4025,4030 and 4035 may include judgments, watches, changes and comments, forexample.

At module 4050, the forecast undergoes executive review. This mayinvolve a review of high-level information (company-wide ororganization-wide performance), and may further include drilling down tomore specific information in some parts of the forecast. Moreover,executive updates to the forecast may be provided at module 4055. Suchupdates may reflect expectations for the economy, customer relationshipsand overall business, or may reflect executive expectations about actualperformance (as opposed to forecasted performance) by various parts ofthe company. Entry of information at module 4055 may include judgmentsand watches, along with other annotations. At module 4060, anotherbaseline is generated, incorporating executive changes to the forecast.

Feedback is then provided to stakeholders, such as those originallyentering information and those entering later changes, at module 4065.This feedback may include annotations and associated updates toforecasts. Note, during the process of entering data, watches may havealerted people to some changes already. Module 4065 may implement a moreformal or thorough notification. Objections to changes may be receivedfrom stakeholders at module 4070. Such objections may relate todifferences of opinions, or to a need to meet organizational andpersonal commitments to clients or customers, for example. Similarly,revisions to the forecast may be received at module 4075. Objections andrevisions may require authorization or approval for actual entry, or maybe entered without intervention in various embodiments. Yet anotherbaseline may then be generated at module 4080.

Actual performance data may then be collected at module 4085. Thus,actual sales figures or performance information may be collected. Atmodule 4090, actual performance information may then be compared to thevarious baselines. Moreover, reports of differences between execution(actual data) and forecasts may be generated for use by various users.

Note that comparison is illustrated at the end of the process.Comparison of various baselines during the process may also be useful.Moreover, determination of why numbers in various baselines do not matchmay allow for avoidance of hidden problems, for example. Thus, theprocess may be manipulated by users in various embodiments to achieveuseful business results.

Reviewing data collection in a timeline model may also be illustrative.FIG. 41 illustrates a timeline of operations in conjunction with areal-time forecasting system in one embodiment. Timeline 4100illustrates data gathering and baseline generation in the process in oneembodiment. Sales forecast data gathering occurs, and is exemplified bydata collection events 4110 and 4115. These may be deadlines forforecast reporting, for example. Baseline generation event 4120 may markthe end of sale forecast collection.

Internal review may include review by marketing, finance and operationsgroups, for example. Data collection events 4125, 4165 and 4175illustrate various collection events and data entry by users or groupsof users. Some data collection may involve group meetings to decide onwhat data is entered, other data collection may be individual. Abaseline is generated at event 4130, which may be an internal baseline,for example.

Executive review may involve further data collection at events 4135 and4160. Such data may be collected based on developments in the business,meetings with clients, or other events. An executive baseline is thegenerated at event 4140. Execution then occurs, although some executionmay have been occurring on an ongoing basis (resulting in businessdevelopments for example). Actual data is gathered at events 4145, 4170and 4150, and an actual baseline is generated at event 4150 to allow forcomparisons to the prior baselines.

Further Technical Features

Feedback, baselines and annotations may all be supported by varioustechnologies. FIG. 42 illustrates an embodiment of a data structure.Data structure 4200 represents data in a data base, with various tablesor dimensions of the data illustrated. Other data may also be involved,depending on implementations and database schema in various embodiments.

Fact data 4210 is the actual data collected in a system. This may befacts such as number of units to be shipped to a customer, for example.User data 4230 is related data indicating where the fact came from.Customer data 4230 is related data indicating which customer the factdatum of 4210 relates to. Product data 4240 is related data indicatingwhich product (or service) the fact datum of 4210 relates to. Thus, asales person may enter the number 400, representing a number of units orprice of units or similar information. From the user interface (wherethe data was entered), the customer and product may be determined.Similarly, the identity of the user may come from a login procedure.Additional data such as the time of the entry, the timing of the data(when the goods will be shipped or service will be rendered), the typeof entry (units, currency, volume, etc.) May be collected and stored aswell.

Accompanying the database may be an OLAP structure as discussedpreviously. FIG. 43 illustrates an embodiment of an OLAP structure. TheOLAP system 4300 is illustrated with two partitions, though the numberof partitions may vary based on various implementation details. In thisembodiment, a first partition 4310 includes general data from adatabase. This partition is updated periodically, such as once a day,for example. A second partition 4320 includes change data. Thispartition is updated much more frequently than partition 4310, and mayreceive data from a database on a continuous basis, for example.

Updating the OLAP structure includes both adding data and calculatingthe effects of that data. Thus, partition 4320, containing change data,may be calculated frequently without using too many resources due to thesmall amount of data included, whereas partition 4310 with much moredata may be calculated and added to less frequently. Changes 4330 aresent to partition 4320, such as from a database. This system may performparticularly well in response to a query 4340. Such a query may be sentto both partitions, receiving data from both, and having the dataaggregated to appear as a single response.

Making the changes in an OLAP structure is part of the process ofproviding a fast query response. FIG. 44 illustrates an embodiment of aprocess of making changes in an OLAP structure. Process 4400 includesreceiving data, entering the data in a database with a flag set,processing the change partition, determining if a periodic update shouldoccur, processing all partitions, clearing flags, and using a unifiedpartition.

A fact datum (new data point or change to data) is received at module4410. At module 4420, the datum is recorded in a database, with a flagindicating a change has occurred—the recorded datum is new. At module4430, all data in the database which is flagged is then processed intothe change partition of an OLAP cube. Since this is likely to be a smallamount of data relative to the contents of the database, this results inrelatively low consumption of resources. Thus, the OLAP cube has apartition with changed data in it, and that data populates the cube toallow for access to information in a speedy fashion.

At module 4440, a determination is made as to whether the entire OLAPcube should be updated. This may occur in conjunction with a timer 4445,which may be set to alert to a need for update on a daily or otherpredetermined basis, for example. If no update is due, the processreturns to module 4410 for the next change. If an update is due, changeddata is provided to all partitions, and all partitions are processed atmodule 4450. All flags related to changed data are also cleared atmodule 4460. This results in a unified partition at module 4470, whichmay be used to answer queries. As changes accumulate, queries will tendto invoke both the change partition and the previously unifiedpartition, through the same process 4400.

With the data in the OLAP structure, queries may be sent and responsivedata supplied. FIG. 45 illustrates an embodiment of a process ofresponding to a query in an OLAP structure. Process 4500 includesreceiving a query, sending the query to both cubes, receiving data,aggregating the data, combining the results, and providing a userresult.

A query is received at module 4510, requesting data from the OLAP cubeand associated database—and which will be serviced from the OLAP cube.Preferably, the query is then sent in parallel to the two cubes(partitions) or in a nearly parallel manner. At module 4520, the queryis passed to the main cube. At module 4530, responsive data is receivedfrom the main cube, and at module 4540, that data is aggregated into aresult.

Correspondingly, at module 4545, a determination is made as to whetherany changes are in the change cube. If not, the response of module 4540is complete at module 4580. If changes are present, the query is sent tothe change cube at module 4550. At module 4560, data is received fromthe change cube responsive to the query. At module 4570, that data isaggregated, and at module 4580, data from the two cubes is combined intoa single response. At module 4590 that response is then provided to theuser. Aggregation may involve computing final data or combining parts ofdata which were not previously combined with the cube, or organizingdata from a cube into a more useful format, for example.

Data from the cubes and database may be organized based on baselines.FIG. 46 illustrates an embodiment of a data structure in conjunctionwith a timeline. Data structure 4600 is illustrated in conjunction withtimeline 4650 to illustrate how baseline information may be stored.

Data structure 4600 includes facts 4610, customer information 4620, userinformation 4630, and other associated information. Data structure 4600also includes window information 4640. Thus, for each fact datum infacts 4610, a customer, user, and window is associated—although thisdata need not be absolutely populated for all data.

Timeline 4650 illustrates a timeline for a monthly cycle or baselines. Afirst baseline or field baseline is set at day 7, and designated aswindow #1. A second baseline is set at day 15, and designated as window#2. A third baseline is set at day 20, and designated as window #3. Foreach of these windows, data entered prior to the baseline is includedand data entered after is excluded. Thus, a fact datum may have a windownumber associated with it, indicating when it was entered (along with adate stamp which is not shown). Alternatively, date stamps of data maybe used in baseline calculation, and windows 4640 may store the set ofbaselines and associated timestamps for comparisons.

Windows may also be designated privately, thus indicating thatadditional changes were made in a what-if type of scenario and shouldnot be retained for all forecasts, but should be used in a separatebaseline for purposes of experimentation with forecasts. Thus, a window21 may be designated, for example, with changes associated therewith,and that window 21 and associated baseline may be used for comparisonwith other baselines. However, changes associated with window 21 may notenter into changes associated with other windows, for example. Moreover,window 21 may be based on a preceding window, and the window it is basedon may be changed over time as desired by a user.

Data from the OLAP cubes may be requested and received in a variety ofways. Understanding how requests and data gathering may interact mayalso help. FIG. 47 illustrates an embodiment of a process of respondingto a request for data. Process 4700 includes requesting data, gettingoriginal data, gathering changes, and displaying a response. FIG. 48illustrates an embodiment of a process of storing a change in data.Process 4800 includes receiving a change, gathering surrounding data,and storing the change.

Thus, a change may be received at module 4810. Surrounding data is thencollected (or comes with the change) at module 4820. Surrounding data toa change may be data such as a customer, user, window in which thechange was made, product, region, time the change is applicable to(tomorrow, a forecast three months out, for example), and time thechange is made, for example. The window may be designated by the user ormay default to the window in which the system is to make changes at thetime. The data of the change, both actual fact data and surroundingdata, is stored at module 4830.

In the meantime, at module 4725, a request for data is received. Atmodule 4740, original data related to the request is retrieved, such asfrom a main OLAP cube partition. At module 4750, changes to the data aregathered, such as at a change OLAP cube partition. The combined responseis then displayed at module 4760. If the request includes an indicationof a window to be used, data for that window is provided, along withdata used as building blocks from prior windows. A default window may beimposed, including all current data, too.

The window specified may dictate that some changes are excluded, eitherbecause they are private (specific to a private window) or because thewindow is private and does not include all changes in a forecast.However, a request may also be set up to include a selection of windows,allowing a user to cut out changes made by the executive suite ormarketing group to determine if results are better that way, forexample. Similarly, a response may provide a display of the changinginformation over time—indicating values at various windows, for example.Moreover, if the request of module 4725 is transmitted when changes arebeing stored at module 4830, the request may overlap the change storage,but the result may include the changes as long as sufficient time isallowed to collect changes.

Understanding how information may be applied in this system to varioussituations may provide further assistance. FIG. 49 illustrates anembodiment of a set of baselines and tracked data. System 4900 includesatomic data and various associated baselines. Data points 4910 areorganized for convenience based on users who would use them. Thus, a CEOhas an analyst target, a personal target and an internal target. A VP ofsales has an official target, a personal target, a sales manager(internal sales group) target and a representative (external salesgroup) target. Similarly, a regional sales manager may have an officialtarget and a personal target.

Various baselines are prepared. Sales baseline 4950 and company baseline4930 are public baselines for use within the company for forecasts.Regional manager baseline 4960, VP of sales baseline 4940 and CEObaseline 4920 are personal baselines created to track alternativescenarios or goals which the various users do not wish to have public.Thus, the personal targets are associated with private baselines, andthe various public targets are associated with public baselines.

However, these various numbers may each build off of a common historyand common set of numbers which are aggregated to arrive at the variousexpectations. Similarly, the baselines may build selectively off ofother baselines—and baselines in general may be expected to depend fromearlier baselines in terms of the data used and drawn from forcalculations. Thus, each user may have a unique view of the data andstill all look at (essentially) the same data. If baselines selectivelybuild from earlier baselines, the information about which baselines areincluded will generally be accessible when such baseline information isused.

A system may be used to implement the various embodiments, allowing forreal-time forecasting. FIG. 50 illustrates an embodiment of a medium andassociated devices. System 5000 includes medium 5010, OLAP engine 5080and cube 5070, database 5060, and user device 5090. Medium 5010 includesa database interface 5040, an OLAP interface 5030, a user interface5050, and a control module 5020. OLAP engine 5080 and OLAP cube 5070provide the OLAP data structure, which is populated from the data indatabase 5060 using OLAP engine 5080. User device 5090 provides the userentry into the system, for queries and responses.

Thus, a query may go to user interface 5050, and then to control 5020.The query may then go to OLAP interface 5030, to get data from OLAP cube5070, and then relay that data through control module 5020 and userinterface 5050 to the user. Similarly, new data or change data may gofrom user interface 5050 to control module 5020 and then to databaseinterface 5040. Database interface 5040 may then submit the data todatabase 5060, with the data then populated into a change partition ofOLAP cube 5070. Eventually, the partitions of OLAP cube 5070 areunified, and the change data is thereby integrated.

From the foregoing, it will be appreciated that specific embodiments ofthe invention have been described herein for purposes of illustration,but that various modifications may be made without deviating from thespirit and scope of the invention. In some instances, reference has beenmade to characteristics likely to be present in various or someembodiments, but these characteristics are also not necessarily limitingon the spirit and scope of the invention. In the illustrations anddescription, structures have been provided which may be formed orassembled in other ways within the spirit and scope of the invention.Moreover, in general, features from one embodiment may be used withother embodiments mentioned in this document provided the features arenot somehow mutually exclusive.

In particular, the separate modules of the various block diagramsrepresent functional modules of methods or apparatuses and are notnecessarily indicative of physical or logical separations or of an orderof operation inherent in the spirit and scope of the present invention.Similarly, methods have been illustrated and described as linearprocesses, but such methods may have operations reordered or implementedin parallel within the spirit and scope of the invention. Accordingly,the invention is not limited except as by the appended claims.

1. A system comprising: an analysis server adapted for coupling to at least an external first client and incorporating an analytics and aggregation unit to analyze and update the information in a first customer database; and a first database of information coupled to the analysis server, the first database embodying forecast data and receiving updates to the forecast data, the analytics and aggregation unit operable to analyze and update the information of at least a first partition of the first database.
 2. The system of claim 1, frurther comprising the at least an external first client.
 3. The system of claim 1, wherein the database comprises a customer database storing forecast data for a first customer.
 4. The system of claim 3, wherein the database comprises a customer database storing forecast data for a second customer.
 5. The system of claim 1, wherein the database is a database shared by a plurality of customers, including for a first customer and a second customer.
 6. The system of claim 1, wherein the change partition is aggregated by the analysis server.
 7. The system of claim 1, wherein the database includes a first partition and a second partition, the first partition including change data and the second partition including general data.
 8. The system of claim 1, wherein updating the database includes both adding or changing data and calculating the effects of that added or changed data.
 9. The system of claim 1, wherein the first partition containing change data may be calculated frequently without using too many analysis server resources due to the small amount of change data included in that second partition.
 10. The system of claim 1, wherein analysis server and the database are configured to use the change data in the first partition so that the system only operates on the changed data and performs particularly well in response to a query.
 11. The system of claim 10, wherein the query may be communicated to both the first and second partitions and accessing data from both partitions, and having the data aggregated to appear as a single query response.
 12. The system of claim 1, wherein the first partition includes only changed data, the amount of data in the first partition is smaller than the amount of data in the second partition, so that there is a relatively low consumption of resources of the analytics and aggregation unit in analyzing and updating the information and in responding to a query, and so that access to the data is accomplished more quickly that if responding to the query required a higher consumption of resources of the analytics and aggregation unit in analyzing and updating the information in the second partition.
 13. The system of claim 1, wherein the system includes provides a world wide web interface and a world wide web hosted forecasting application.
 14. The system of claim 1, wherein the system provides for concurrent access by multiple organizations.
 15. The system of claim 14, wherein for the multiple organizations, the data in the database is segregated for each different one of the multiple organizations to satisfy confidentiality concerns.
 16. The system of claim 15, wherein the first database includes multiple data repositories that may be separate, either logically or physically, or both logically and physically.
 17. The system of claim 1, wherein the an analytics and aggregation unit processing facilities are separated, logically, or physically, or both logically and physically.
 18. The system of claim 14, wherein: a first user may use a first client to access the system, and access a first data repository using the analysis server including the analytics and aggregation unit; and a second user may use a second client to access the system, and access a second data repository.
 19. The system of claim 14, wherein the first user and the second user use the same the analytics and aggregation unit.
 20. The system of claim 14, wherein the first user and the second user use a different the analytics and aggregation unit.
 21. The system of claim 14, wherein the first user and the second user use a different instantiation of the same analytics and aggregation unit.
 22. A method comprising: providing an analysis server incorporating an analytics and aggregation unit for analyzing information in a first database; coupling the analysis server to at least an external first client and to a first database, the at least an external first client configured to communicate a query request to the analysis server; and operating the analytics and aggregation unit to aggregate only changed information into a change partition of the database and to analyze and update the information only on the basis of the changed data in the change partition.
 23. The method of claim 22, wherein the change partition containing change data may be calculated frequently without using too many analysis server resources due to the small amount of change data included in that change partition.
 24. The method of claim 22, wherein analysis server and the database are configured to use the change data in the change partition so that the system only operates on the changed data and performs particularly well in response to a query.
 25. The method of claim 24, wherein the query may be communicated to both the change partition and to a second main partition and accessing data from both partitions, and having the data aggregated to appear as a single query response.
 26. The method of claim 25, wherein the change partition includes only changed data, the amount of data in the change partition is smaller than the amount of data in the second partition, so that there is a relatively low consumption of resources of the analytics and aggregation unit in analyzing and updating the information and in responding to a query, and so that access to the data is accomplished more quuicky that if responding to the query required a higher consumption of resources of the analytics and aggregation unit in analyzing and updating the information in the second main partition.
 27. The method of claim 22, wherein the system provides a world wide web interface and a world wide web hosted forecasting application.
 28. The method of claim 22, wherein the system provides for concurrent access by multiple organizations.
 29. The method of claim 28, wherein for the multiple organizations, the data in the database is segregated for each different one of the multiple organizations to satisfy confidentiality concerns.
 30. The method of claim 28, wherein: a first user may use a first client to access the system, and access a first data repository using the analysis server including the analytics and aggregation unit; and a second user may use a second client to access the system, and access a second data repository.
 31. The method of claim 30, wherein the first user and the second user use the same analytics and aggregation unit.
 32. The method of claim 30, wherein the first user and the second user use a different the analytics and aggregation unit or a different instantiation of the same analytics and aggregation unit. 